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A deep learning-based approach for predicting oil production: A case study in the United States

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Listed:
  • Du, Jian
  • Zheng, Jianqin
  • Liang, Yongtu
  • Ma, Yunlu
  • Wang, Bohong
  • Liao, Qi
  • Xu, Ning
  • Ali, Arshid Mahmood
  • Rashid, Muhammad Imtiaz
  • Shahzad, Khurram

Abstract

The accuracy of oil production predictions is crucial in the field of petroleum engineering. However, due to the time series characteristics of oil production and the complex relationship among multiple influencing factors, traditional methods, and time series prediction techniques have limitations in fully extracting time series features and exploring the internal relationships between variables. Deep learning techniques possess unique advantages in solving nonlinear problems and time-series problems but require a significant amount of data and can only conduct short-term oil production predictions due to the nonlinear and chaotic nature of yield. Therefore, this work aims to establish a model that can overcome these limitations. A modified GRU (M-GRU) is proposed to extract time series features of variables and external economic information. Then, the autoregressive (AR) model is integrated into importing priori knowledge to enhance the model's capability in extracting time series information. In addition, feature mapping is used to improve convergence performance. Finally, A loss function that can dynamically adjust the weights is proposed, which can enhance the model's ability to fit from error-prone samples. By validating the proposed model using oil production data, it has been proven that the model can predict oil production accurately and outperform other models with a correlation coefficient reaching 0.99874. Further tests show that the model can provide accurate prediction results even with limited sample sizes and during coincidental events such as financial crises and COVID-19 epidemics, providing strong support for decision-making for reservoir engineers.

Suggested Citation

  • Du, Jian & Zheng, Jianqin & Liang, Yongtu & Ma, Yunlu & Wang, Bohong & Liao, Qi & Xu, Ning & Ali, Arshid Mahmood & Rashid, Muhammad Imtiaz & Shahzad, Khurram, 2024. "A deep learning-based approach for predicting oil production: A case study in the United States," Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223030827
    DOI: 10.1016/j.energy.2023.129688
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    References listed on IDEAS

    as
    1. Cavicchioli, Maddalena, 2023. "Statistical analysis of Markov switching vector autoregression models with endogenous explanatory variables," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    2. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Lu, Xinyi & Tu, Renfu & Liao, Qi & Xu, Ning & Xia, Yuheng, 2023. "A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction," Energy, Elsevier, vol. 263(PD).
    3. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Xu, Ning & Klemeš, Jiří Jaromír & Wang, Bohong & Liao, Qi & Varbanov, Petar Sabev & Shahzad, Khurram & Ali, Arshid Mahmood, 2023. "Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution," Energy, Elsevier, vol. 276(C).
    4. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).
    5. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Xia, Yuheng & Wang, Bohong & Shao, Qi & Liao, Qi & Tu, Renfu & Xu, Bin & Xu, Ning, 2023. "Deeppipe: An intelligent framework for predicting mixed oil concentration in multi-product pipeline," Energy, Elsevier, vol. 282(C).
    6. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
    7. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
    8. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
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    1. Yuan, Ziyun & Chen, Lei & Liu, Gang & Li, Zukui & Wu, Yuchen & Pan, Yuanhao & Ji, Haoyang & Yang, Wen, 2024. "Soft sensor development for mixed oil interface tracking in multi-product pipelines based on knowledge-informed semi-supervised Variational Bayesian Gaussian mixture regression," Energy, Elsevier, vol. 300(C).

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